Intelligent Car Cockpit Comfort Evaluation Model Based on SVM
With the popularization of intelligent cars, users’ understanding of the value of cars gradually changes from a travel tool to a “third living space”, and cabin comfort is becoming a criterion for evaluating the goodness of cars. In this paper, we start from the phys...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10440081/ |
| _version_ | 1827339990801055744 |
|---|---|
| author | Fei Chen Hongbo Shi Jianjun Yang Yikang Li Yu Lai |
| author_facet | Fei Chen Hongbo Shi Jianjun Yang Yikang Li Yu Lai |
| author_sort | Fei Chen |
| collection | DOAJ |
| description | With the popularization of intelligent cars, users’ understanding of the value of cars gradually changes from a travel tool to a “third living space”, and cabin comfort is becoming a criterion for evaluating the goodness of cars. In this paper, we start from the physical environment and human-computer interaction environment that affect the comfort of the intelligent cockpit of a car and establish a comprehensive comfort evaluation model of the intelligent cockpit of a car based on the support vector machine (SVM) algorithm in machine learning by conducting experiments on the comfort evaluation of the intelligent cockpit of a car and compare it with several classical machine learning algorithms. The mean square error (<inline-formula> <tex-math notation="LaTeX">$MSE$ </tex-math></inline-formula>) of the model based on the SVM algorithm is 0.00096, and the coefficient of determination (<inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>) reaches 0.966, which is better than several other algorithms. The results show that the established evaluation model has good generalization ability and can evaluate the comprehensive comfort of the intelligent cockpit of the car, thus helping the cockpit to make timely and accurate comfort adjustments to ensure the occupant’s riding experience. This project provides a reference direction for the comprehensive evaluation of cockpit comfort, which is of great significance for the future development of intelligent cockpit comfort. In addition, the comfort model can be applied to a variety of comfort evaluation scenarios, which has great practical value. |
| first_indexed | 2024-03-07T20:10:35Z |
| format | Article |
| id | doaj.art-213f0ae706e847be9434102849096417 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-07T20:10:35Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-213f0ae706e847be94341028490964172024-02-28T00:00:39ZengIEEEIEEE Access2169-35362024-01-0112275662757710.1109/ACCESS.2024.336732510440081Intelligent Car Cockpit Comfort Evaluation Model Based on SVMFei Chen0https://orcid.org/0009-0005-6883-2836Hongbo Shi1https://orcid.org/0000-0002-6524-6613Jianjun Yang2https://orcid.org/0000-0002-4760-5184Yikang Li3https://orcid.org/0009-0002-7203-4413Yu Lai4https://orcid.org/0000-0002-9532-9225School of Automobile and Transportation, Xihua University, Chengdu, ChinaSchool of Automobile and Transportation, Xihua University, Chengdu, ChinaSchool of Automobile and Transportation, Xihua University, Chengdu, ChinaSchool of Automobile and Transportation, Xihua University, Chengdu, ChinaSchool of Mechanical Engineering, Xihua University, Chengdu, ChinaWith the popularization of intelligent cars, users’ understanding of the value of cars gradually changes from a travel tool to a “third living space”, and cabin comfort is becoming a criterion for evaluating the goodness of cars. In this paper, we start from the physical environment and human-computer interaction environment that affect the comfort of the intelligent cockpit of a car and establish a comprehensive comfort evaluation model of the intelligent cockpit of a car based on the support vector machine (SVM) algorithm in machine learning by conducting experiments on the comfort evaluation of the intelligent cockpit of a car and compare it with several classical machine learning algorithms. The mean square error (<inline-formula> <tex-math notation="LaTeX">$MSE$ </tex-math></inline-formula>) of the model based on the SVM algorithm is 0.00096, and the coefficient of determination (<inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>) reaches 0.966, which is better than several other algorithms. The results show that the established evaluation model has good generalization ability and can evaluate the comprehensive comfort of the intelligent cockpit of the car, thus helping the cockpit to make timely and accurate comfort adjustments to ensure the occupant’s riding experience. This project provides a reference direction for the comprehensive evaluation of cockpit comfort, which is of great significance for the future development of intelligent cockpit comfort. In addition, the comfort model can be applied to a variety of comfort evaluation scenarios, which has great practical value.https://ieeexplore.ieee.org/document/10440081/SVMmachine learningintelligent car cockpitcomfortcomprehensive evaluation |
| spellingShingle | Fei Chen Hongbo Shi Jianjun Yang Yikang Li Yu Lai Intelligent Car Cockpit Comfort Evaluation Model Based on SVM IEEE Access SVM machine learning intelligent car cockpit comfort comprehensive evaluation |
| title | Intelligent Car Cockpit Comfort Evaluation Model Based on SVM |
| title_full | Intelligent Car Cockpit Comfort Evaluation Model Based on SVM |
| title_fullStr | Intelligent Car Cockpit Comfort Evaluation Model Based on SVM |
| title_full_unstemmed | Intelligent Car Cockpit Comfort Evaluation Model Based on SVM |
| title_short | Intelligent Car Cockpit Comfort Evaluation Model Based on SVM |
| title_sort | intelligent car cockpit comfort evaluation model based on svm |
| topic | SVM machine learning intelligent car cockpit comfort comprehensive evaluation |
| url | https://ieeexplore.ieee.org/document/10440081/ |
| work_keys_str_mv | AT feichen intelligentcarcockpitcomfortevaluationmodelbasedonsvm AT hongboshi intelligentcarcockpitcomfortevaluationmodelbasedonsvm AT jianjunyang intelligentcarcockpitcomfortevaluationmodelbasedonsvm AT yikangli intelligentcarcockpitcomfortevaluationmodelbasedonsvm AT yulai intelligentcarcockpitcomfortevaluationmodelbasedonsvm |